Pathological OCT Retinal Layer Segmentation Using Branch Residual U-Shape Networks

  • Stefanos ApostolopoulosEmail author
  • Sandro De Zanet
  • Carlos Ciller
  • Sebastian Wolf
  • Raphael Sznitman
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10435)


The automatic segmentation of retinal layer structures enables clinically-relevant quantification and monitoring of eye disorders over time in OCT imaging. Eyes with late-stage diseases are particularly challenging to segment, as their shape is highly warped due to pathological biomarkers. In this context, we propose a novel fully-Convolutional Neural Network (CNN) architecture which combines dilated residual blocks in an asymmetric U-shape configuration, and can segment multiple layers of highly pathological eyes in one shot. We validate our approach on a dataset of late-stage AMD patients and demonstrate lower computational costs and higher performance compared to other state-of-the-art methods.

Supplementary material

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Supplementary material 1 (pdf 19529 KB)


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Stefanos Apostolopoulos
    • 1
    Email author
  • Sandro De Zanet
    • 2
  • Carlos Ciller
    • 2
  • Sebastian Wolf
    • 3
  • Raphael Sznitman
    • 1
  1. 1.University of BernBernSwitzerland
  2. 2.RetinAI Medical GmbHBernSwitzerland
  3. 3.University Hospital of BernBernSwitzerland

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